title: Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding.
abstract: BACKGROUND
  Most functions within the cell emerge thanks to protein-protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable.
  
  
  METHODS
  Network embedding emphasizes the relations between network proteins embedded in a low-dimensional space, in which protein pairs that are closer to each other represent good candidate interactions. To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced space to assign likelihood scores to candidate interactions. Furthermore, we introduce (i) a new valid variation of MCE, named non-centred MCE (ncMCE); (ii) two automatic strategies for selecting the appropriate embedding dimension; and (iii) two new randomized procedures for evaluating predictions.
  
  
  RESULTS
  We compared our method against several unsupervised and supervisedly tuned embedding approaches and node neighbourhood techniques. Despite its computational simplicity, ncMCE-SP was the overall leader, outperforming the current methods in topological link prediction.
  
  
  CONCLUSIONS
  Minimum curvilinearity is a valuable non-linear framework that we successfully applied to the embedding of protein networks for the unsupervised prediction of novel PPIs. The rationale for our approach is that biological and evolutionary information is imprinted in the non-linear patterns hidden behind the protein network topology, and can be exploited for predicting new protein links. The predicted PPIs represent good candidates for testing in high-throughput experiments or for exploitation in systems biology tools such as those used for network-based inference and prediction of disease-related functional modules.
  
  
  BACKGROUND
  https://sites.google.com/site/carlovittoriocannistraci/home.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Integrative Systems Biology Laboratory, Biological and Environmental Sciences and Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia. kalokagathos.agon@gmail.com
authors.forename: Carlo Vittorio
authors.initials: CV
authors.lastname: Cannistraci
authors.forename: Gregorio
authors.initials: G
authors.lastname: Alanis-Lobato
authors.forename: Timothy
authors.initials: T
authors.lastname: Ravasi
mesh_terms: D000465:Algorithms; D063990:Gene Ontology; D025941:Protein Interaction Mapping / Q000379:methods*; D049490:Systems Biology